Tune Translation LLM models by using supervised fine-tuning

This document describes how to tune a Translation LLM model by using supervisedfine-tuning.

Before you begin

Before you begin, you must prepare a supervised fine-tuning dataset.Depending on your use case, there are different requirements.

Supported Models

  • translation-llm-002 (In preview, only supports text tuning)

Create a tuning job

You can create a supervised fine-tuning job by using the REST API orthe Vertex AI SDK for Python.

REST

To create a model tuning job, send a POST request by using thetuningJobs.createmethod. Some of the parameters are not supported by all of the models. Ensurethat you include only the applicable parameters for the model that you'retuning.

Before using any of the request data, make the following replacements:

  • PROJECT_ID: Yourproject ID.
  • TUNING_JOB_REGION: Theregion where the tuning job runs. This is also the default region for where the tuned model is uploaded. Supported region:us-central1.
  • BASE_MODEL: Name of the translation model to tune. Supported values:translation-llm-002.
  • TRAINING_DATASET_URI: Cloud Storage URI of your training dataset. The dataset must be formatted as a JSONL file. For best results, provide at least 100 to 500 examples. For more information, seeAbout supervised tuning dataset.
  • VALIDATION_DATASET_URIOptional: The Cloud Storage URI of your validation dataset file.
  • TUNED_MODEL_DISPLAYNAMEOptional: A display name for the tuned model. If not set, a random name is generated.

HTTP method and URL:

POST https://TUNING_JOB_REGION-aiplatform.googleapis.com/v1/projects/PROJECT_ID/locations/TUNING_JOB_REGION/tuningJobs

Request JSON body:

{  "baseModel": "BASE_MODEL",  "supervisedTuningSpec" : {      "trainingDatasetUri": "TRAINING_DATASET_URI",      "validationDatasetUri": "VALIDATION_DATASET_URI",  },  "tunedModelDisplayName": "TUNED_MODEL_DISPLAYNAME"}

To send your request, choose one of these options:

curl

Note: The following command assumes that you have logged in to thegcloud CLI with your user account by runninggcloud init orgcloud auth login , or by usingCloud Shell, which automatically logs you into thegcloud CLI . You can check the currently active account by runninggcloud auth list.

Save the request body in a file namedrequest.json, and execute the following command:

curl -X POST \
     -H "Authorization: Bearer $(gcloud auth print-access-token)" \
     -H "Content-Type: application/json; charset=utf-8" \
     -d @request.json \
     "https://TUNING_JOB_REGION-aiplatform.googleapis.com/v1/projects/PROJECT_ID/locations/TUNING_JOB_REGION/tuningJobs"

PowerShell

Note: The following command assumes that you have logged in to thegcloud CLI with your user account by runninggcloud init orgcloud auth login . You can check the currently active account by runninggcloud auth list.

Save the request body in a file namedrequest.json, and execute the following command:

$cred = gcloud auth print-access-token
$headers = @{ "Authorization" = "Bearer $cred" }

Invoke-WebRequest `
    -Method POST `
    -Headers $headers `
    -ContentType: "application/json; charset=utf-8" `
    -InFile request.json `
    -Uri "https://TUNING_JOB_REGION-aiplatform.googleapis.com/v1/projects/PROJECT_ID/locations/TUNING_JOB_REGION/tuningJobs" | Select-Object -Expand Content

You should receive a JSON response similar to the following.

Response

{  "name": "projects/PROJECT_ID/locations/TUNING_JOB_REGION/tuningJobs/TUNING_JOB_ID",  "createTime":CREATE_TIME,  "updateTime":UPDATE_TIME,  "status": "STATUS",  "supervisedTuningSpec": {        "trainingDatasetUri": "TRAINING_DATASET_URI",        "validationDatasetUri": "VALIDATION_DATASET_URI",    },  "tunedModelDisplayName": "TUNED_MODEL_DISPLAYNAME"}

Example curl command

PROJECT_ID=myprojectLOCATION=us-central1curl\-XPOST\-H"Authorization: Bearer$(gcloudauthprint-access-token)"\-H"Content-Type: application/json; charset=utf-8"\"https://${LOCATION}-aiplatform.googleapis.com/v1/projects/${PROJECT_ID}/locations/${LOCATION}/tuningJobs"\-d\$'{   "baseModel": "translation-llm-002",   "supervisedTuningSpec" : {      "training_dataset_uri": "gs://cloud-samples-data/ai-platform/generative_ai/gemini-2_0/text/sft_train_data.jsonl",      "validation_dataset_uri": "gs://cloud-samples-data/ai-platform/generative_ai/gemini-2_0/text/sft_validation_data.jsonl"   },   "tunedModelDisplayName": "tuned_translation_llm"}'

Python

fromvertexai.generative_modelsimportGenerativeModelsft_tuning_job=sft.SupervisedTuningJob("projects/<PROJECT_ID>/locations/<TUNING_JOB_REGION>/tuningJobs/<TUNING_JOB_ID>")tuned_model=GenerativeModel(sft_tuning_job.tuned_model_endpoint_name)print(tuned_model.generate_content(content))importtimeimportvertexaifromvertexai.tuningimportsft# TODO(developer): Update and un-comment below line.# PROJECT_ID = os.environ["GOOGLE_CLOUD_PROJECT"]vertexai.init(project=PROJECT_ID,location="us-central1")sft_tuning_job=sft.train(source_model="translation-llm-002",train_dataset="gs://cloud-samples-data/ai-platform/generative_ai/gemini-2_0/text/sft_train_data.jsonl",# The following parameters are optionalvalidation_dataset="gs://cloud-samples-data/ai-platform/generative_ai/gemini-2_0/text/sft_validation_data.jsonl",tuned_model_display_name="tuned_translation_llm_002",)# Polling for job completionwhilenotsft_tuning_job.has_ended:time.sleep(60)sft_tuning_job.refresh()print(sft_tuning_job.tuned_model_name)print(sft_tuning_job.tuned_model_endpoint_name)print(sft_tuning_job.experiment)# Example response:# projects/123456789012/locations/us-central1/models/1234567890@1# projects/123456789012/locations/us-central1/endpoints/123456789012345# <google.cloud.aiplatform.metadata.experiment_resources.Experiment object at 0x7b5b4ae07af0>

View a list of tuning jobs

You can view a list of tuning jobs in your current project by using the Google Cloud console,the Vertex AI SDK for Python, or by sending a GET request by using thetuningJobs method.

REST

To view a list of model tuning jobs, send a GET request by using thetuningJobs.listmethod.

Before using any of the request data, make the following replacements:

  • PROJECT_ID: Yourproject ID.
  • TUNING_JOB_REGION: Theregion where the tuning job runs. This is also the default region for where the tuned model is uploaded.

HTTP method and URL:

GET https://TUNING_JOB_REGION-aiplatform.googleapis.com/v1/projects/PROJECT_ID/locations/TUNING_JOB_REGION/tuningJobs

To send your request, choose one of these options:

curl

Note: The following command assumes that you have logged in to thegcloud CLI with your user account by runninggcloud init orgcloud auth login , or by usingCloud Shell, which automatically logs you into thegcloud CLI . You can check the currently active account by runninggcloud auth list.

Execute the following command:

curl -X GET \
     -H "Authorization: Bearer $(gcloud auth print-access-token)" \
     "https://TUNING_JOB_REGION-aiplatform.googleapis.com/v1/projects/PROJECT_ID/locations/TUNING_JOB_REGION/tuningJobs"

PowerShell

Note: The following command assumes that you have logged in to thegcloud CLI with your user account by runninggcloud init orgcloud auth login . You can check the currently active account by runninggcloud auth list.

Execute the following command:

$cred = gcloud auth print-access-token
$headers = @{ "Authorization" = "Bearer $cred" }

Invoke-WebRequest `
    -Method GET `
    -Headers $headers `
    -Uri "https://TUNING_JOB_REGION-aiplatform.googleapis.com/v1/projects/PROJECT_ID/locations/TUNING_JOB_REGION/tuningJobs" | Select-Object -Expand Content

You should receive a JSON response similar to the following.

Response

{  "tuning_jobs": [TUNING_JOB_1,TUNING_JOB_2, ...  ]}

Python

importvertexaifromvertexai.tuningimportsft# TODO(developer): Update and un-comment below line# PROJECT_ID = "your-project-id"vertexai.init(project=PROJECT_ID,location="us-central1")responses=sft.SupervisedTuningJob.list()forresponseinresponses:print(response)# Example response:# <vertexai.tuning._supervised_tuning.SupervisedTuningJob object at 0x7c85287b2680># resource name: projects/12345678/locations/us-central1/tuningJobs/123456789012345

Console

To view your tuning jobs in the Google Cloud console, go to theVertex AI Studio page.

Go toVertex AI Studio

Your Translation LLM tuning jobs are listed in the table under theTranslation LLM tuned modelssection.

Get details of a tuning job

You can get the details of a tuning job in your current projectby using the Google Cloud console, the Vertex AI SDK for Python, or by sending a GETrequest by using thetuningJobs method.

REST

To view a list of model tuning jobs, send a GET request by using thetuningJobs.getmethod and specify theTuningJob_ID.

Before using any of the request data, make the following replacements:

  • PROJECT_ID: .
  • TUNING_JOB_REGION: Theregion where the tuning job runs. This is also the default region for where the tuned model is uploaded.
  • TUNING_JOB_ID: The ID of the tuning job.

HTTP method and URL:

GET https://TUNING_JOB_REGION-aiplatform.googleapis.com/v1/projects/PROJECT_ID/locations/TUNING_JOB_REGION/tuningJobs/TUNING_JOB_ID

To send your request, choose one of these options:

curl

Note: The following command assumes that you have logged in to thegcloud CLI with your user account by runninggcloud init orgcloud auth login , or by usingCloud Shell, which automatically logs you into thegcloud CLI . You can check the currently active account by runninggcloud auth list.

Execute the following command:

curl -X GET \
     -H "Authorization: Bearer $(gcloud auth print-access-token)" \
     "https://TUNING_JOB_REGION-aiplatform.googleapis.com/v1/projects/PROJECT_ID/locations/TUNING_JOB_REGION/tuningJobs/TUNING_JOB_ID"

PowerShell

Note: The following command assumes that you have logged in to thegcloud CLI with your user account by runninggcloud init orgcloud auth login . You can check the currently active account by runninggcloud auth list.

Execute the following command:

$cred = gcloud auth print-access-token
$headers = @{ "Authorization" = "Bearer $cred" }

Invoke-WebRequest `
    -Method GET `
    -Headers $headers `
    -Uri "https://TUNING_JOB_REGION-aiplatform.googleapis.com/v1/projects/PROJECT_ID/locations/TUNING_JOB_REGION/tuningJobs/TUNING_JOB_ID" | Select-Object -Expand Content

You should receive a JSON response similar to the following.

Response

{  "name": "projects/PROJECT_ID/locations/TUNING_JOB_REGION/tuningJobs/TUNING_JOB_ID",  "tunedModelDisplayName": "TUNED_MODEL_DISPLAYNAME",  "createTime":CREATE_TIME,  "endTime":END_TIME,  "tunedModel": {      "model": "projects/PROJECT_ID/locations/TUNING_JOB_REGION/models/MODEL_ID",      "endpoint": "projects/PROJECT_ID/locations/TUNING_JOB_REGION/endpoints/ENDPOINT_ID"  },  "experiment": "projects/PROJECT_ID/locations/TUNING_JOB_REGION/metadataStores/default/contexts/EXPERIMENT_ID",  "tuning_data_statistics": {      "supervisedTuningDataStats": {          "tuninDatasetExampleCount": "TUNING_DATASET_EXAMPLE_COUNT",          "totalBillableTokenCount": "TOTAL_BILLABLE_TOKEN_COUNT",          "tuningStepCount": "TUNING_STEP_COUNT"      }  },  "status": "STATUS",  "supervisedTuningSpec" : {        "trainingDatasetUri": "TRAINING_DATASET_URI",        "validationDataset_uri": "VALIDATION_DATASET_URI",        "hyperParameters": {            "epochCount":EPOCH_COUNT,            "learningRateMultiplier":LEARNING_RATE_MULTIPLIER        }    }}

Python

importvertexaifromvertexai.tuningimportsft# TODO(developer): Update and un-comment below lines# PROJECT_ID = "your-project-id"# LOCATION = "us-central1"vertexai.init(project=PROJECT_ID,location=LOCATION)tuning_job_id="4982013113894174720"response=sft.SupervisedTuningJob(f"projects/{PROJECT_ID}/locations/{LOCATION}/tuningJobs/{tuning_job_id}")print(response)# Example response:# <vertexai.tuning._supervised_tuning.SupervisedTuningJob object at 0x7cc4bb20baf0># resource name: projects/1234567890/locations/us-central1/tuningJobs/4982013113894174720

Console

  1. To view details of a tuned model in the Google Cloud console, go to theVertex AI Studio page.

    Go toVertex AI Studio

  2. In theTranslation LLM tuned models table, find your model and clickDetails.

    The details of your model are shown.

Cancel a tuning job

You can cancel a tuning job in your current project by using the Google Cloud console,the Vertex AI SDK for Python, or by sending a POST request using thetuningJobs method.

REST

To view a list of model tuning jobs, send a GET request by using thetuningJobs.cancelmethod and specify theTuningJob_ID.

Before using any of the request data, make the following replacements:

  • PROJECT_ID: .
  • TUNING_JOB_REGION: Theregion where the tuning job runs. This is also the default region for where the tuned model is uploaded.
  • TUNING_JOB_ID: The ID of the tuning job.

HTTP method and URL:

POST https://TUNING_JOB_REGION-aiplatform.googleapis.com/v1/projects/PROJECT_ID/locations/TUNING_JOB_REGION/tuningJobs/TUNING_JOB_ID:cancel

To send your request, choose one of these options:

curl

Note: The following command assumes that you have logged in to thegcloud CLI with your user account by runninggcloud init orgcloud auth login , or by usingCloud Shell, which automatically logs you into thegcloud CLI . You can check the currently active account by runninggcloud auth list.

Execute the following command:

curl -X POST \
     -H "Authorization: Bearer $(gcloud auth print-access-token)" \
     -H "Content-Type: application/json; charset=utf-8" \
     -d "" \
     "https://TUNING_JOB_REGION-aiplatform.googleapis.com/v1/projects/PROJECT_ID/locations/TUNING_JOB_REGION/tuningJobs/TUNING_JOB_ID:cancel"

PowerShell

Note: The following command assumes that you have logged in to thegcloud CLI with your user account by runninggcloud init orgcloud auth login . You can check the currently active account by runninggcloud auth list.

Execute the following command:

$cred = gcloud auth print-access-token
$headers = @{ "Authorization" = "Bearer $cred" }

Invoke-WebRequest `
    -Method POST `
    -Headers $headers `
    -Uri "https://TUNING_JOB_REGION-aiplatform.googleapis.com/v1/projects/PROJECT_ID/locations/TUNING_JOB_REGION/tuningJobs/TUNING_JOB_ID:cancel" | Select-Object -Expand Content

You should receive a JSON response similar to the following.

Response

{}

Python

importvertexaifromvertexai.tuningimportsft# TODO(developer): Update and un-comment below lines# PROJECT_ID = "your-project-id"# LOCATION = "us-central1"vertexai.init(project=PROJECT_ID,location=LOCATION)tuning_job_id="4982013113894174720"job=sft.SupervisedTuningJob(f"projects/{PROJECT_ID}/locations/{LOCATION}/tuningJobs/{tuning_job_id}")job.cancel()

Console

  1. To cancel a tuning job in the Google Cloud console, go to theVertex AI Studio page.

    Go toVertex AI Studio

  2. In theTranslation tuned models table, clickManage run.

  3. ClickCancel.

Test the tuned model with a prompt

You can test a tuning job in your current project by using theVertex AI SDK for Python or by sending a POST request using thetuningJobsmethod.

The following example prompts a model with the question "Why is sky blue?".

REST

To test a tuned model with a prompt, send a POST request andspecify theTUNED_ENDPOINT_ID.

Before using any of the request data, make the following replacements:

  • PROJECT_ID: .
  • TUNING_JOB_REGION: The region where the tuning job runs. This is also the default region for where the tuned model is uploaded.
  • ENDPOINT_ID: The tuned model endpoint ID from the GET API.

HTTP method and URL:

POST https://TUNING_JOB_REGION-aiplatform.googleapis.com/v1/projects/PROJECT_ID/locations/TUNING_JOB_REGION/endpoints/ENDPOINT_ID:generateContent

Request JSON body:

{    "contents": [        {            "role": "USER",            "parts": {                "text" : "English: Hello. Spanish:"            }        }    ],}

To send your request, choose one of these options:

curl

Note: The following command assumes that you have logged in to thegcloud CLI with your user account by runninggcloud init orgcloud auth login , or by usingCloud Shell, which automatically logs you into thegcloud CLI . You can check the currently active account by runninggcloud auth list.

Save the request body in a file namedrequest.json, and execute the following command:

curl -X POST \
     -H "Authorization: Bearer $(gcloud auth print-access-token)" \
     -H "Content-Type: application/json; charset=utf-8" \
     -d @request.json \
     "https://TUNING_JOB_REGION-aiplatform.googleapis.com/v1/projects/PROJECT_ID/locations/TUNING_JOB_REGION/endpoints/ENDPOINT_ID:generateContent"

PowerShell

Note: The following command assumes that you have logged in to thegcloud CLI with your user account by runninggcloud init orgcloud auth login . You can check the currently active account by runninggcloud auth list.

Save the request body in a file namedrequest.json, and execute the following command:

$cred = gcloud auth print-access-token
$headers = @{ "Authorization" = "Bearer $cred" }

Invoke-WebRequest `
    -Method POST `
    -Headers $headers `
    -ContentType: "application/json; charset=utf-8" `
    -InFile request.json `
    -Uri "https://TUNING_JOB_REGION-aiplatform.googleapis.com/v1/projects/PROJECT_ID/locations/TUNING_JOB_REGION/endpoints/ENDPOINT_ID:generateContent" | Select-Object -Expand Content

You should receive a JSON response similar to the following.

Response

{  "candidates": [    {      "content": {        "role": "model",        "parts": [English: Hello. Spanish:          {            "text": "Hola."          }        ]      },      "finishReason": "STOP",    }  ],  "usageMetadata": {    "promptTokenCount": 5,    "candidatesTokenCount": 33,    "totalTokenCount": 38  }}

Python

fromvertexai.generative_modelsimportGenerativeModelsft_tuning_job=sft.SupervisedTuningJob("projects/<PROJECT_ID>/locations/<TUNING_JOB_REGION>/tuningJobs/<TUNING_JOB_ID>")tuned_model=GenerativeModel(sft_tuning_job.tuned_model_endpoint_name)print(tuned_model.generate_content(content))

Tuning and validation metrics

You can configure a model tuning job to collect and report model tuning andmodel evaluation metrics, which can then be visualized inVertex AI Studio.

  1. To view details of a tuned model in the Google Cloud console, go to theVertex AI Studio page.

    Go toVertex AI Studio

  2. In theTune and Distill table, click the name of the tuned model that you want to view metrics for.

    The tuning metrics appear under theMonitor tab.

Model tuning metrics

The model tuning job automatically collects the following tuning metricsfortranslation-llm-002.

  • /train_total_loss: Loss for the tuning dataset at a training step.
  • /train_fraction_of_correct_next_step_preds: The token accuracy at a trainingstep. A single inference consists of a sequence of predicted tokens. This metricmeasures the accuracy of the predicted tokens when compared to the groundtruth in the tuning dataset.
  • /train_num_predictions: Number of predicted tokens at a training step.

Model validation metrics:

You can configure a model tuning job to collect the following validation metricsfortranslation-llm-002.

  • /eval_total_loss: Loss for the validation dataset at a validation step.
  • /eval_fraction_of_correct_next_step_preds: The token accuracy at anvalidation step. A single inference consists of a sequence of predicted tokens. Thismetric measures the accuracy of the predicted tokens when compared to theground truth in the validation dataset.
  • /eval_num_predictions: Number of predicted tokens at a validation step.

The metrics visualizations are available after the tuning job starts running.It will be updated in real time as tuning progresses.If you don't specify a validation dataset when you create the tuning job, onlythe visualizations for the tuning metrics are available.

What's next

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Last updated 2025-11-24 UTC.